Assessing the Effects of Fuel Moisture Content on the 2018 Megafires in California

Kang, Zhenyu; Quan, Xingwen; Lai, Gengke

doi:10.1109/jstars.2022.3232665

Cited by 13 publications

(3 citation statements)

References 67 publications

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“…This serves as a good indicator of the drying trends in these medium fuels, which are crucial for the sustained burning of fires. The DMC also ranges from 0 to a high value, typically approximately 300, with higher numbers indicating drier conditions and a greater potential for a fire to consume deeper fuel layers and persist once ignited [32,33].…”

Section: Meteorological Datamentioning

confidence: 99%

Development of a Forest Fire Diagnostic Model Based on Machine Learning Techniques

Roh,

Lee,

et al. 2024

Forests

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Forest fires have devastating effects on extensive forest areas, compromising vital ecological services such as air purification, water conservation, and recreational opportunities, thus posing a significant socioeconomic threat. Furthermore, the risk of forest fires is steadily increasing due to climate change. The most effective method for mitigating forest fire risk is proactive prevention before forest fires can occur by identifying high-risk areas based on land surface conditions. This study aimed to develop a machine learning-based forest fire diagnostic model designed for South Korea, considering both satellite-derived land surface data and anthropogenic factors. For the remote sensing data, VTCI (Vegetation Temperature Condition Index) was used to reflect the land surface dryness. In addition, fire activity maps for buildings, roads and cropland were used to consider the influence of human activities. The forest fire diagnostic model yielded an accuracy of 0.89, demonstrating its effectiveness in predicting forest fire risk. To validate the effectiveness of the model, 92 short-term forest fire risk forecast maps were generated from March to May 2023 with real-time data on forest fire occurrences collected for verification. The results showed that 73% of forest fires were accurately classified within high-risk zones, confirming the operational accuracy of the model. Through the forest fire diagnostic model, we have presented the impact relationships of meteorological, topographical, and environmental data, as well as the dryness index based on satellite images and anthropogenic factors, on forest fire occurrence. Additionally, we have demonstrated the potential uses of surface condition data.

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Section: Meteorological Datamentioning

confidence: 99%

Development of a Forest Fire Diagnostic Model Based on Machine Learning Techniques

Roh,

Lee,

et al. 2024

Forests

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“…The impacts of live fuel moisture content (LFMC) and dead fuel moisture content (DFMC) on the 2018 California mega re was investigated and it demonstrated a correlation between the parameters. The DFMC played a signi cant in uence in causing the 2018 mega-re in California, although the LFMC exhibited the highest link with the occurrence of re between 2001 and 2018 [9].…”

Section: Introductionmentioning

confidence: 97%

Network of Drones for Early Warning of Forest Fire and Dynamic Fire Quenching Plan Generation

Manoj

Valliyammai

2023

Preprint

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Wildfires, one of the most frequent natural disasters, significantly harm society, the economy, and the environment. Transfer learning algorithm and modern machine learning tools can help in early forest fire prediction, detection, and dynamic fire quenching. Also, a dynamic fire quenching plan is prescribed which takes into account the live status of forest fire. A network of drones which has high definition image processing and decision making capabilities are used for detection of forest fire in the very early stage there by preventing the forest fire. In case fire breaks out, the proposed system generates a fire quenching plan and alerts the fire and rescue departments so that they do plan early and stop the forest fire as soon as possible. ResNet, VGGNet, Mobile Net, Alexi Net, GoogLeNet models enhanced with transfer learning are used to predict the forest fire events in India. It is found that the proposed technique GoogLeNet-TL, which uses transfer learning with GoogLeNet has resulted in 96% accuracy and 97.56% F1 score than other models considered for forest fire prediction in Indian subcontinent.

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“…Research in [86,87] provided in-depth information on fuel moisture codes for this In [91][92][93], the researchers studied the role of forest fuel moisture content in forest fires. In a recent study, Li et al [94], proposed a model to apply Ground Water Level (GWL) to FWI.…”

Section: Fuel Moisture Codesmentioning

confidence: 99%

Mutli-Modality Federate Learning with Multi-Source Data for Forest Fire Prediction

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Forest fires result in significant destruction of natural resources and human lives.They are commonly caused by humans or naturally occurring phenomena like lightning strikes. They may start due to lightning when necessary climatic conditions prevail for a the fire to ignite. The available forest fire data contains both fire and non-fire data and is highly imbalanced. To overcome this, the thesis provides a spatio-temporal agnostic subsampling (STAS) framework to subsample the highly imbalanced forest fire data. The proposed framework also works with limited variations between fire and non-fire data. Apart from lightning, weather and drought conditions are also significant factors in forest fires. Forest fire prediction which does not take these into consideration, may not be accurate. Therefore, the thesis is also focused on building and applying a multi-modality modeling approach for predicting forest fires. The multi-modality model is built using available datasets on forest fires, lightning, weather, and hydrometrics to predict the probability of forest fires. Using the proposed STAS framework and multi-modality model, the research aimed to generate a generalized model by applying multi-modality federated learning to predict forest fires independent of geographic location without the need for calibration.This research was able to successfully work with federated learning framework and multi-modality model to obtain high F 1Score (> 0.9) and R 2 Score (> 0.8).

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Assessing the Effects of Fuel Moisture Content on the 2018 Megafires in California

Cited by 13 publications

References 67 publications

Development of a Forest Fire Diagnostic Model Based on Machine Learning Techniques

Development of a Forest Fire Diagnostic Model Based on Machine Learning Techniques

Network of Drones for Early Warning of Forest Fire and Dynamic Fire Quenching Plan Generation

Mutli-Modality Federate Learning with Multi-Source Data for Forest Fire Prediction

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